Senior Machine Learning Engineer, Biologics Discovery

ppAt Johnson Johnson,we believe health is everything. Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented, treated, and cured,where treatments are smarter and less invasive, andsolutions are personal.
Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity.
Learn more at jnj.com. /ppAs guided by Our Credo, Johnson Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson Johnson, we respect the diversity and dignity of our employees and recognize their merit. /ph3Job Function /h3pRD Product Development /ph3Job Sub Function /h3pRD Machine Learning /ph3Job Category /h3pScientific/Technology /ph3All Job Posting Locations /h3pBeerse, Antwerp, Belgium, Madrid, Spain, Raritan, New Jersey, United States of America, Spring House, Pennsylvania, United States of America, Titusville, New Jersey, United States of America /ph3Job Description /h3pOur expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow. /ppLearn more at /ph3About The Opportunity /h3pJohnson Johnson Innovative Medicine is seeking a bSenior ML Engineer /b for our Biologics Discovery Data Science team. This role builds and operates the integration, deployment, lifecycle management, and governance capabilities that enable machine learning (ML) models and AI solutions developed by partner organizations to run reliably in Biologics Discovery environments. You are the senior team member who closes the gap between model-ready data in our data warehouse and models that serve discovery scientists. /ph3we recommend focusing on the specific country(s) that align with your preferred location(s): /h3pUSA
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- /ppSpain
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- /ppBelgium
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- /ph3Why This Role Matters /h3pThe future of AI-native discovery depends on high-quality AI and data solutions that connect scientific data, machine learning models, and agentic workflows. This role will shape how enterprise AI and MLOps capabilities are adapted, integrated, and operationalized for Biologics Discovery, enabling AI solutions to scale from prototypes into trusted capabilities that accelerate scientific learning and therapeutic discovery. /ph3Position Summary /h3pIn this role, you will enable AI/ML solutions to move reliably from development into production within Biologics Discovery. Working closely with data scientists, AI/ML scientists, discovery scientists, and partner organizations at JJ, you will own the deployment, lifecycle management, access, monitoring, and governance of ML, generative AI, and agentic solutions for discovery workflows. The role does not own core model development or the underlying enterprise platforms. Instead, it ensures that models and AI capabilities developed by partner teams are operationalized reliably for scientific use. /ppYou will bring expertise in modern AI/ML operational practices, including reproducibility, CI/CD, observability, governance, automation, and scalable compute, helping adapt enterprise capabilities for discovery-specific use cases. Your work will enable reliable, production-grade AI workflows and accelerate the adoption of ML and agentic systems in biologics discovery. /ph3Why This Role Is Unique /h3pThis is a rare opportunity to play a key role in enabling AI-native Biologics Discovery. You will help operationalize and scale AI/ML capabilities that transform model-ready data and promising models into reliable, production-grade solutions that accelerate scientific discovery. /ph3Key Responsibilities /h3h3AI/ML Operations Lifecycle Management /h3ulliBuild and operate scalable pipelines and interfaces that deliver model-ready data to ML, generative AI, and agentic workflows. /liliEnable closed-loop scientific learning by ensuring newly generated scientific data can be captured, governed, and made available to downstream modeling, evaluation, and agentic workflows. /liliEstablish reliable operational capabilities for model deployment, serving, monitoring, access management, and lifecycle management across development and production environments. /liliImplement model, data, and workflow versioning, with reproducible releases, rollback capabilities, and traceability across the AI/ML lifecycle. /li /ulh3Reliability, Observability Scale /h3ulliEstablish monitoring, observability, alerting, and performance management practices for ML workflows, deployed models, and AI services. /liliDevelop and maintain automated workflows supporting testing, release management, environment management, and operational exc